{"id":2276,"date":"2026-04-09T16:43:25","date_gmt":"2026-04-09T20:43:25","guid":{"rendered":"https:\/\/wp.well-e.org\/publication\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\/"},"modified":"2026-04-28T22:45:56","modified_gmt":"2026-04-29T02:45:56","slug":"multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning","status":"publish","type":"publication","link":"https:\/\/wp.well-e.org\/fr\/publication\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\/","title":{"rendered":"Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning"},"content":{"rendered":"<p><strong>Authors:<\/strong> Maryam Ben Driss and Essaid Sabi and Halima Elbiaze and Abdoulaye Banir\u00e9 Diallo<\/p>\n<p><strong>Date:<\/strong> 2025-12-01<\/p>\n<p><strong>Status:<\/strong> Published<\/p>\n<p><strong>DOI:<\/strong> 10.1109\/GLOBECOM59602.2025.11431785<\/p>\n<p><a href=\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/11431785\" target=\"_blank\" rel=\"noopener\">View External Publication<\/a><\/p>\n<hr>\n<p>Over-the-air federated learning (OTA-FL) is a communication-efficient paradigm that leverages the superposition property of wireless channels to aggregate client updates simultaneously, significantly reducing uplink latency and bandwidth usage. While OTA-FL offers advantages in scalability and speed, it poses challenges in energy efficiency and delay management. This paper proposes a multi-attribute client selection framework that addresses these challenges through a multi-objective optimization approach. We analytically model selection attributes: energy efficiency, communication delay, loss, and fairness, and formulate three optimization problems to capture different trade-offs. To solve them, we employ the Multi-Objective Grey Wolf Optimizer (MOGWO), a nature-inspired metaheuristic algorithm that effectively balances exploration and exploitation. Experiments on MNIST, Fashion MNIST, and CIFAR-10 demonstrate that our approach outperforms baseline and loss-aware methods, achieving up to 13% energy savings while improving model accuracy, fairness, and reliability.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false,"_crdt_document":""},"class_list":["post-2276","publication","type-publication","status-publish","hentry"],"blocksy_meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning - WELL-E<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wp.well-e.org\/fr\/publication\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\/\" \/>\n<meta property=\"og:locale\" content=\"fr_CA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning - WELL-E\" \/>\n<meta property=\"og:description\" content=\"Authors: Maryam Ben Driss and Essaid Sabi and Halima Elbiaze and Abdoulaye Banir\u00e9 Diallo Date: 2025-12-01 Status: Published DOI: 10.1109\/GLOBECOM59602.2025.11431785 View External Publication Over-the-air federated learning (OTA-FL) is a communication-efficient paradigm that leverages the superposition property of wireless channels to aggregate client updates simultaneously, significantly reducing uplink latency and bandwidth usage. While OTA-FL offers advantages [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/wp.well-e.org\/fr\/publication\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"WELL-E\" \/>\n<meta property=\"article:modified_time\" content=\"2026-04-29T02:45:56+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\\\/\",\"url\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\\\/\",\"name\":\"Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning - WELL-E\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/#website\"},\"datePublished\":\"2026-04-09T20:43:25+00:00\",\"dateModified\":\"2026-04-29T02:45:56+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\\\/#breadcrumb\"},\"inLanguage\":\"fr-CA\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/wp.well-e.org\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Publications\",\"item\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/#website\",\"url\":\"https:\\\/\\\/wp.well-e.org\\\/\",\"name\":\"WELL-E\",\"description\":\"IA au service du bien-&ecirc;tre animal\",\"publisher\":{\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/wp.well-e.org\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-CA\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/#organization\",\"name\":\"WELL-E\",\"url\":\"https:\\\/\\\/wp.well-e.org\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-CA\",\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/wp.well-e.org\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/LOGOTYPETAG_Left_RVB_FR-edited-1-e1718738358443.png\",\"contentUrl\":\"https:\\\/\\\/wp.well-e.org\\\/wp-content\\\/uploads\\\/2024\\\/05\\\/LOGOTYPETAG_Left_RVB_FR-edited-1-e1718738358443.png\",\"width\":1000,\"height\":250,\"caption\":\"WELL-E\"},\"image\":{\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/company\\\/research-and-innovation-chair-in-animal-welfare-and-artificial-intelligence-well-e\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning - WELL-E","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/wp.well-e.org\/fr\/publication\/multi-objective-multi-attribute-client-selection-for-sustainable-over-the-air-federated-learning\/","og_locale":"fr_CA","og_type":"article","og_title":"Multi-objective Multi-Attribute Client Selection for Sustainable Over-The-Air Federated Learning - WELL-E","og_description":"Authors: Maryam Ben Driss and Essaid Sabi and Halima Elbiaze and Abdoulaye Banir\u00e9 Diallo Date: 2025-12-01 Status: Published DOI: 10.1109\/GLOBECOM59602.2025.11431785 View External Publication Over-the-air federated learning (OTA-FL) is a communication-efficient paradigm that leverages the superposition property of wireless channels to aggregate client updates simultaneously, significantly reducing uplink latency and bandwidth usage. 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